基于HTS数据与AFC数据的公交出行特征比较研究  

Deep Comparison of Public Transit Characteristic Based on HTS Data and AFC Data

作　　者：雷棵蘩 LEI Kefan(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学,交通运输与物流学院,四川成都611756

出　　处：《综合运输》2024年第6期34-40,143,共8页China Transportation Review

摘　　要：随着大数据时代到来,传统小数据抽样分析逐步向大数据整体分析转变。在智能公交领域,自动收费系统(AFC)每日可产生海量公交乘客出行数据,与传统抽样方式获取的居民出行调查(HTS)数据在来源上相互独立。以2016年成都市HTS数据与AFC数据为例,分别从出发到达时间分布、出行频次分布宏观指标,以及交通小区的出发到达量分布和OD矩阵多个角度进行综合对比分析。结果显示,两类数据在宏观层面上基本相似,但HTS数据有更大风险遗漏非通勤出行,并且当空间分辨率的要求足够高时,HTS会出现覆盖率不足,数据渗透不够等问题。With the advent of the big data era,traditional small data sampling analysis is gradually shifting towards large-scale data analysis.In the field of intelligent public transportation,the Automatic Fare Collection system(AFC)generates massive amounts of data on bus passenger travel on a daily basis,which is independent of the Household Travel Survey(HTS)data obtained through traditional sampling methods.Taking the 2016 Chengdu HTS data and AFC data as examples,this study conducted a comprehensive comparative analysis from multiple perspectives,including macro indicators such as departure and arrival time distribution,travel frequency distribution,as well as the distribution of departure and arrival volume in traffic zones and the OD matrix.The results showed that the two types of data are basically similar at the macro level,but the HTS data is more likely to miss non-commuter trips,and when the spatial resolution requirements are high enough,there may be problems with insufficient coverage and data penetration.

关 键 词：智能交通系统 AFC数据 HTS数据 OD矩阵 出行特征 

分 类 号：U491[交通运输工程—交通运输规划与管理]